OpenRaiser/Intern-Atlas huggingface dataset:
Does a Daily Deal Promotion Signal a Distressed Business? An Empirical Investigation of Small Business Survival
Venue: arXiv cs.CE 2012
Year: 2012

In the last four years, daily deals have emerged from nowhere to become a multi-billion dollar industry world-wide. Daily deal sites such as Groupon and Livingsocial offer products and services at deep discounts to consumers via email and social networks. As the industry matures, there are many questions regarding the impact of daily deals on the marketplace. Important questions in this regard concern the reasons why businesses decide to offer daily deals and their longer-term impact on businesses. In the present paper, we investigate whether the unobserved factors that make marketers run daily deals are correlated with the unobserved factors that influence the business, In particular, we employ the framework of seemingly unrelated regression to model the correlation between the errors in predicting whether a business uses a daily deal and the errors in predicting the business' survival. Our analysis consists of the survival of 985 small businesses that offered daily deals between January and July 2011 in the city of Chicago. Our results indicate that there is a statistically significant correlation between the unobserved factors that influence the business' decision to offer a daily deal and the unobserved factors that impact its survival. Furthermore, our results indicate that the correlation coefficient is significant in certain business categories (e.g. restaurants).
Linear Antenna Array with Suppressed Sidelobe and Sideband Levels using Time Modulation
Venue: arXiv cs.NE 2012
Year: 2012

In this paper, the goal is to achieve an ultra low sidelobe level (SLL) and sideband levels (SBL) of a time modulated linear antenna array. The approach followed here is not to give fixed level of excitation to the elements of an array, but to change it dynamically with time. The excitation levels of the different array elements over time are varied to get the low sidelobe and sideband levels. The mathematics of getting the SLL and SBL furnished in detail and simulation is done using the mathematical results. The excitation pattern over time is optimized using Genetic Algorithm (GA). Since, the amplitudes of the excitations of the elements are varied within a finite limit, results show it gives better sidelobe and sideband suppression compared to previous time modulated arrays with uniform amplitude excitations.
A Comparative study of Arabic handwritten characters invariant feature
Venue: arXiv cs.CV 2012
Year: 2012

This paper is practically interested in the unchangeable feature of Arabic handwritten character. It presents results of comparative study achieved on certain features extraction techniques of handwritten character, based on Hough transform, Fourier transform, Wavelet transform and Gabor Filter. Obtained results show that Hough Transform and Gabor filter are insensible to the rotation and translation, Fourier Transform is sensible to the rotation but insensible to the translation, in contrast to Hough Transform and Gabor filter, Wavelets Transform is sensitive to the rotation as well as to the translation.
Fine Residual Carrier Frequency and Sampling Frequency Estimation in Wireless OFDM Systems
Venue: arXiv cs.IT 2012
Year: 2012

This paper presents a novel algorithm for residual phase estimation in wireless OFDM systems, including the carrier frequency offset (CFO) and the sampling frequency offset (SFO). The subcarriers are partitioned into several regions which exhibit pairwise correlations. The phase increment between successive OFDM blocks is exploited which can be estimated by two estimators with different computational loads. Numerical results of estimation variance are presented. Simulations indicate performance improvement of the proposed technique over several conventional schemes in a multipath channel.
STAR-Vote: A Secure, Transparent, Auditable, and Reliable Voting System
Venue: arXiv cs.CR 2012
Year: 2012

In her 2011 EVT/WOTE keynote, Travis County, Texas County Clerk Dana DeBeauvoir described the qualities she wanted in her ideal election system to replace their existing DREs. In response, in April of 2012, the authors, working with DeBeauvoir and her staff, jointly architected STAR-Vote, a voting system with a DRE-style human interface and a "belt and suspenders" approach to verifiability. It provides both a paper trail and end-to-end cryptography using COTS hardware. It is designed to support both ballot-level risk-limiting audits, and auditing by individual voters and observers. The human interface and process flow is based on modern usability research. This paper describes the STAR-Vote architecture, which could well be the next-generation voting system for Travis County and perhaps elsewhere.
A Decision Support Tool for Inferring Further Education Desires of Youth in Sri Lanka
Venue: arXiv cs.CY 2012
Year: 2012

This paper presents the results of a study carried out to identify the factors that influence the further education desires of Sri Lankan youth. Statistical modeling has been initially used to infer the desires of the youth and then a decision support tool has been developed based on the statistical model developed. In order to carry out the analysis and the development of the model, data collected as part of the National Youth Survey has been used. The accuracy of the model and the decision support tool has been tested by using a random data sets and the accuracy was found to be well above 80 percent, which is sufficient for any policy related decision making.
LAGE: A Java Framework to reconstruct Gene Regulatory Networks from Large-Scale Continues Expression Data
Venue: arXiv cs.LG 2012
Year: 2012

LAGE is a systematic framework developed in Java. The motivation of LAGE is to provide a scalable and parallel solution to reconstruct Gene Regulatory Networks (GRNs) from continuous gene expression data for very large amount of genes. The basic idea of our framework is motivated by the philosophy of divideand-conquer. Specifically, LAGE recursively partitions genes into multiple overlapping communities with much smaller sizes, learns intra-community GRNs respectively before merge them altogether. Besides, the complete information of overlapping communities serves as the byproduct, which could be used to mine meaningful functional modules in biological networks.
Secured Wireless Communication using Fuzzy Logic based High Speed Public-Key Cryptography (FLHSPKC)
Venue: arXiv cs.CR 2012
Year: 2012

In this paper secured wireless communication using fuzzy logic based high speed public key cryptography (FLHSPKC) has been proposed by satisfying the major issues likes computational safety, power management and restricted usage of memory in wireless communication. Wireless Sensor Network (WSN) has several major constraints likes inadequate source of energy, restricted computational potentiality and limited memory. Though conventional Elliptic Curve Cryptography (ECC) which is a sort of public key cryptography used in wireless communication provides equivalent level of security like other existing public key algorithm using smaller parameters than other but this traditional ECC does not take care of all these major limitations in WSN. In conventional ECC consider Elliptic curve point p, an arbitrary integer k and modulus m, ECC carry out scalar multiplication kP mod m, which takes about 80% of key computation time on WSN. In this paper proposed FLHSPKC scheme provides some novel strategy including novel soft computing based strategy to speed up scalar multiplication in conventional ECC and which in turn takes shorter computational time and also satisfies power consumption restraint, limited usage of memory without hampering the security level. Performance analysis of the different strategies under FLHSPKC scheme and comparison study with existing conventional ECC methods has been done.
Abstract flows over time: A first step towards solving dynamic packing problems
Venue: arXiv cs.DM 2012
Year: 2012

Flows over time generalize classical network flows by introducing a notion of time. Each arc is equipped with a transit time that specifies how long flow takes to traverse it, while flow rates may vary over time within the given edge capacities. In this paper, we extend this concept of a dynamic optimization problem to the more general setting of abstract flows. In this model, the underlying network is replaced by an abstract system of linearly ordered sets, called "paths" satisfying a simple switching property: Whenever two paths P and Q intersect, there must be another path that is contained in the beginning of P and the end of Q. We show that a maximum abstract flow over time can be obtained by solving a weighted abstract flow problem and constructing a temporally repeated flow from its solution. In the course of the proof, we also show that the relatively modest switching property of abstract networks already captures many essential properties of classical networks.
IS-LABEL: an Independent-Set based Labeling Scheme for Point-to-Point Distance Querying on Large Graphs
Venue: arXiv cs.DB 2012
Year: 2012

We study the problem of computing shortest path or distance between two query vertices in a graph, which has numerous important applications. Quite a number of indexes have been proposed to answer such distance queries. However, all of these indexes can only process graphs of size barely up to 1 million vertices, which is rather small in view of many of the fast-growing real-world graphs today such as social networks and Web graphs. We propose an efficient index, which is a novel labeling scheme based on the independent set of a graph. We show that our method can handle graphs of size three orders of magnitude larger than those existing indexes.
Mining Determinism in Human Strategic Behavior
Venue: arXiv cs.GT 2012
Year: 2012

This work lies in the fusion of experimental economics and data mining. It continues author's previous work on mining behaviour rules of human subjects from experimental data, where game-theoretic predictions partially fail to work. Game-theoretic predictions aka equilibria only tend to success with experienced subjects on specific games, what is rarely given. Apart from game theory, contemporary experimental economics offers a number of alternative models. In relevant literature, these models are always biased by psychological and near-psychological theories and are claimed to be proven by the data. This work introduces a data mining approach to the problem without using vast psychological background. Apart from determinism, no other biases are regarded. Two datasets from different human subject experiments are taken for evaluation. The first one is a repeated mixed strategy zero sum game and the second - repeated ultimatum game. As result, the way of mining deterministic regularities in human strategic behaviour is described and evaluated. As future work, the design of a new representation formalism is discussed.
Rank-1 Games With Exponentially Many Nash Equilibria
Venue: arXiv cs.GT 2012
Year: 2012

The rank of a bimatrix game (A,B) is the rank of the matrix A+B. We give a construction of rank-1 games with exponentially many equilibria, which answers an open problem by Kannan and Theobald (2010).
The Effects of Website Quality on Adoption of E-Government Service: AnEmpirical Study Applying UTAUT Model Using SEM
Venue: arXiv cs.CY 2012
Year: 2012

In today global age, e-government services have become the main channel for online communication between the government and its citizens. They aim to provide citizens with more accessible, accurate, real-time and high quality services. Therefore, the quality of government websites which provide e-services is an essential factor in the successful adoption of e-government services by the public. This paper discusses an investigation of the effect of the Website Quality (WQ) factor on the acceptance of using e-government services (G2C) in the Kingdom of Saudi Arabia (KSA) by adopting the Unified Theory of Acceptance and Use of Technology (UTAUT) Model. Survey Data collected from 400 respondents were examined using the structural equation modelling (SEM) technique and utilising AMOS tools. This study found that the factors that significantly influenced the Use Behaviour of e-government services in KSA (USE) include Performance Expectancy (PE), Effort expectancy (EE), Facilitating Conditions (FC) and Website Quality (WQ), while the construct known Social Influence (SI) did not. Moreover, the results confirm the importance of quality government websites and support systems as one of the main significant and influential factors of e-government services adoption. The results of this study can be helpful to Saudi governmental sectors to adjust their corporate strategies and plans to advance successful adoption and diffusion of e-government services (G2C) in KSA.
New Edge Detection Technique based on the Shannon Entropy in Gray Level Images
Venue: arXiv cs.CV 2012
Year: 2012

Edge detection is an important field in image processing. Edges characterize object boundaries and are therefore useful for segmentation, registration, feature extraction, and identification of objects in a scene. In this paper, an approach utilizing an improvement of Baljit and Amar method which uses Shannon entropy other than the evaluation of derivatives of the image in detecting edges in gray level images has been proposed. The proposed method can reduce the CPU time required for the edge detection process and the quality of the edge detector of the output images is robust. A standard test images, the real-world and synthetic images are used to compare the results of the proposed edge detector with the Baljit and Amar edge detector method. In order to validate the results, the run time of the proposed method and the pervious method are presented. It has been observed that the proposed edge detector works effectively for different gray scale digital images. The performance evaluation of the proposed technique in terms of the measured CPU time and the quality of edge detector method are presented. Experimental results demonstrate that the proposed method achieve better result than the relevant classic method.
Teichmüller extremal mapping and its applications to landmark matching registration
Venue: arXiv cs.CG 2012
Year: 2012

Registration, which aims to find an optimal 1-1 correspondence between shapes, is an important process in different research areas. Conformal mappings have been widely used to obtain a diffeomorphism between shapes that minimizes angular distortion. Conformal registrations are beneficial since it preserves the local geometry well. However, when landmark constraints are enforced, conformal mappings generally do not exist. This motivates us to look for a unique landmark matching quasi-conformal registration, which minimizes the conformality distortion. Under suitable condition on the landmark constraints, a unique diffeomporphism, called the Teichmüller extremal mapping between two surfaces can be obtained, which minimizes the maximal conformality distortion. In this paper, we propose an efficient iterative algorithm, called the Quasi-conformal (QC) iterations, to compute the Teichmüller mapping. The basic idea is to represent the set of diffeomorphisms using Beltrami coefficients (BCs), and look for an optimal BC associated to the desired Teichmüller mapping. The associated diffeomorphism can be efficiently reconstructed from the optimal BC using the Linear Beltrami Solver(LBS). Using BCs to represent diffeomorphisms guarantees the diffeomorphic property of the registration. Using our proposed method, the Teichmüller mapping can be accurately and efficiently computed within 10 seconds. The obtained registration is guaranteed to be bijective. The proposed algorithm can also be extended to compute Teichmüller mapping with soft landmark constraints. We applied the proposed algorithm to real applications, such as brain landmark matching registration, constrained texture mapping and human face registration. Experimental results shows that our method is both effective and efficient in computing a non-overlap landmark matching registration with least amount of conformality distortion.
A memory versus compression ratio trade-off in PPM via compressed context modeling
Venue: arXiv cs.DS 2012
Year: 2012

Since its introduction prediction by partial matching (PPM) has always been a de facto gold standard in lossless text compression, where many variants improving the compression ratio and speed have been proposed. However, reducing the high space requirement of PPM schemes did not gain that much attention. This study focuses on reducing the memory consumption of PPM via the recently proposed compressed context modeling that uses the compressed representations of contexts in the statistical model. Differently from the classical context definition as the string of the preceding characters at a particular position, CCM considers context as the amount of preceding information that is actually the bit stream composed by compressing the previous symbols. We observe that by using the CCM, the data structures, particularly the context trees, can be implemented in smaller space, and present a trade-off between the compression ratio and the space requirement. The experiments conducted showed that this trade-off is especially beneficial in low orders with approximately 20 - 25 percent gain in memory by a sacrifice of up to nearly 7 percent loss in compression ratio.
Boosting Simple Collaborative Filtering Models Using Ensemble Methods
Venue: arXiv cs.IR 2012
Year: 2012

In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We present an adaptation of several popular ensemble techniques in machine learning for the collaborative filtering domain, including bagging, boosting, fusion and randomness injection. We evaluate the proposed approach on several types of collaborative filtering base models: k- NN, matrix factorization and a neighborhood matrix factorization model. Empirical evaluation shows a prediction improvement compared to all base CF algorithms. In particular, we show that the performance of an ensemble of simple (weak) CF models such as k-NN is competitive compared with a single strong CF model (such as matrix factorization) while requiring an order of magnitude less computational cost.
An Analytic Approach to People Evaluation in Crowdsourcing Systems
Venue: arXiv cs.IR 2012
Year: 2012

Worker selection is a significant and challenging issue in crowdsourcing systems. Such selection is usually based on an assessment of the reputation of the individual workers participating in such systems. However, assessing the credibility and adequacy of such calculated reputation is a real challenge. In this paper, we propose an analytic model which leverages the values of the tasks completed, the credibility of the evaluators of the results of the tasks and time of evaluation of the results of these tasks in order to calculate an accurate and credible reputation rank of participating workers and fairness rank for evaluators. The model has been implemented and experimentally validated.
Genetic Optimization of Keywords Subset in the Classification Analysis of Texts Authorship
Venue: arXiv cs.IR 2012
Year: 2012

The genetic selection of keywords set, the text frequencies of which are considered as attributes in text classification analysis, has been analyzed. The genetic optimization was performed on a set of words, which is the fraction of the frequency dictionary with given frequency limits. The frequency dictionary was formed on the basis of analyzed text array of texts of English fiction. As the fitness function which is minimized by the genetic algorithm, the error of nearest k neighbors classifier was used. The obtained results show high precision and recall of texts classification by authorship categories on the basis of attributes of keywords set which were selected by the genetic algorithm from the frequency dictionary.
Spectral Clustering: An empirical study of Approximation Algorithms and its Application to the Attrition Problem
Venue: arXiv cs.LG 2012
Year: 2012

Clustering is the problem of separating a set of objects into groups (called clusters) so that objects within the same cluster are more similar to each other than to those in different clusters. Spectral clustering is a now well-known method for clustering which utilizes the spectrum of the data similarity matrix to perform this separation. Since the method relies on solving an eigenvector problem, it is computationally expensive for large datasets. To overcome this constraint, approximation methods have been developed which aim to reduce running time while maintaining accurate classification. In this article, we summarize and experimentally evaluate several approximation methods for spectral clustering. From an applications standpoint, we employ spectral clustering to solve the so-called attrition problem, where one aims to identify from a set of employees those who are likely to voluntarily leave the company from those who are not. Our study sheds light on the empirical performance of existing approximate spectral clustering methods and shows the applicability of these methods in an important business optimization related problem.
Report: Error estimation of recovered solution in FE analysis
Venue: arXiv cs.NA 2012
Year: 2012

The recovery type error estimators introduced by Zienkiewicz and Zhu use a recovered stress field evaluated from the Finite Element (FE) solution. Their accuracy depends on the quality of the recovered field. In this sense, accurate results are obtained using recovery procedures based on the Superconvergent Patch recovery technique (SPR). These error estimators can be easily implemented and provide accurate estimates. Another important feature is that the recovered solution is of a better quality than the FE solution and can therefore be used as an enhanced solution. We have developed an SPR-type recovery technique that considers equilibrium and displacements constraints to obtain a very accurate recovered displacements field from which a recovered stress field can also be evaluated. We propose the use of these recovered fields as the standard output of the FE code instead of the raw FE solution. Techniques to quantify the error of the recovered solution are therefore needed. In this report we present an error estimation technique that accurately evaluates the error of the recovered solution both at global and local levels in the FEM and XFEM frameworks. We have also developed an h-adaptive mesh refinement strategy based on the error of the recovered solution. As the converge rate of the error of the recovered solution is higher than that of the FE one, the computational cost required to obtain a solution with a prescribed accuracy is smaller than for traditional h-adaptive processes.
Non regression testing for the JOREK code
Venue: arXiv cs.DC 2012
Year: 2012

Non Regression Testing (NRT) aims to check if software modifications result in undesired behaviour. Suppose the behaviour of the application previously known, this kind of test makes it possible to identify an eventual regression, a bug. Improving and tuning a parallel code can be a time-consuming and difficult task, especially whenever people from different scientific fields interact closely. The JOREK code aims at investing Magnetohydrodynamic (MHD) instabilities in a Tokamak plasma. This paper describes the NRT procedure that has been tuned for this simulation code. Automation of the NRT is one keypoint to keeping the code healthy in a source code repository.